Method for assessing and forecasting the operational health of a telephone network switch

ABSTRACT

A current operational health of a Telephone Network Switch (TNS) is determined by selectively using only certain parameters. These selected parameters, both internal and external to the TNS, are those that most influence the operational health. The operational health of the TNS is projected into the future in order to forecast the TNS performance. Not only can a current or future deviation be determined, but also a source of the deviation is identified allowing for a focused preventative maintenance effort.

FIELD OF THE INVENTION

The present invention relates generally to preventive maintenance intelecommunications systems, and more particularly to assessing anoperational health of a Telephone Network Switch and forecasting itsoperational health into the future.

BACKGROUND OF THE INVENTION

Properly assessing a piece of equipment's operational integrity, hereincalled “operational health”, is a problem that is widespread. The dangerof not accurately assessing a piece of equipment's operational health,i.e. how the equipment is functioning in contrast with its ideal levelof performance, is that the equipment could suddenly fail without anyforewarning. In a case where the equipment plays a critical role in thefunctioning of a large production facility such as an oilrig, or is acritical component in a communications network, such a failure couldlead to a loss of millions of dollars in lost equipment and revenues.

Even if an organization took a cautious stance and decided to perform anexcessive amount of preventive maintenance, this too has a downside.Such maintenance leads to excessive labor costs, and may actuallyincrease the chance of equipment failure due to the faulty performanceof a preventative maintenance procedure.

While there are current technologies for monitoring the operationalhealth of a piece of equipment, these technologies have severalweaknesses. One of the weaknesses is that these methods individuallyevaluate multiple reports and alarms from a piece of equipment, whereeach of these reports or alarms is associated with a different aspect ofthe overall operational health of the equipment. When an individualvalue from the report or alarm falls outside of a particular range, anoperator is alerted. This is a piecemeal approach, in which theequipment is viewed as merely a collection of individual parts. However,an accurate prediction of the operational health of the entire equipmentmay only come to light when these individual reports and alarms areevaluated together. Each feedback value may not reveal a potentialequipment failure that is only uncovered when the overall equipment isevaluated.

U.S. Pat. No. 6,748,341, whose disclosure is incorporated herein byreference, generally involves a method and device for providing anoverall machine health prediction. This is accomplished by generating aset of predictive equations using either historical or real-timecalibration data from one or more normally operating machines ratherthan from the current piece of equipment under evaluation. One of theseequations is selected, and the operational parameters for a piece ofequipment under evaluation are entered into the equation. The calculatedvalue representing the operational health of the equipment underevaluation is compared to a value determined from historical orreal-time data from other normally operating machines. The differencebetween the predicted and actual operation health values is determined.If the difference is statistically significant, an overall probably ofmachine abnormality is determined.

While the '341 patent addresses the problem of determining the current,overall operational health of a piece of equipment, it still leavesseveral weaknesses. First, the '341 patent only teaches comparing acurrent operational health of a piece of equipment with historical orreal-time values of other similar pieces of equipment. However, itdoesn't teach comparing the operational health of a piece of equipmentwith its own historical performance. Using the equipment's ownhistorical performance data is significant because each piece ofequipment has a unique, acceptable operational health range due toequipment specific factors. Some of these factors include: the age ofthe equipment, the environment in which it is being used, and the volumeof usage the equipment experiences. These and other factors could leadto an acceptable operational range that is unique for that piece ofequipment, even in comparison to other similar equipment. Second, the'341 patent only discusses determining the current operational health ofthe equipment without projecting the equipment's performance into thefuture. However, it is useful to know not only the current operationalhealth of a piece of equipment, but also to project a forecast of theequipment's performance into the future. Third, the '341 patent does notteach determining which element of the piece of equipment is likely tocause a deviation in the operational health. It is useful to know whichelement of the equipment is contributing to the deviation in properoperational health in order to effectively focus preventativemaintenance. Fourth, the '341 patent discusses taking “raw data formachine variables of interest” as the data used in determining thecurrent operational status of the equipment. However, an effectivepredictive maintenance tool should not only take into account raw datafor machine variables of interest, but also take into account externalfeedback concerning the equipment's performance, such as reporteduser-complaints, typical capacity-utilization of the equipment, anddiligence in performing established preventive maintenance routines.

An example of a piece of equipment, the knowledge of whose operationalhealth is critical for the organization utilizing the equipment is aTelephone Network Switch (TNS). A TNS is a central part to atelecommunications network which facilitates the routing of a call fromthe calling party to the called party. It is important to detect apotential failure of a TNS before an organization experiences a downedcommunications network due to a failed TNS.

While it is clearly important to know the operational health of a TNS,such a determination may be economically infeasible. A typical TNS isprogrammed to issue dozens and possibly even hundreds of reports andalarms concerning its operational health. It is challenging and laborintensive to monitor each of these feedback values on a continuingbasis. Additionally, several other variables that are not included inthese reports and alarms also play an important role in predicting thehealth of the TNS. Collecting and analyzing the TNS reports and alarmsas well as the other variables not included in these reports and alarmsmay involve too much labor and capital resources to make such amonitoring economically worthwhile.

SUMMARY OF THE INVENTION

In accordance with one aspect of the current invention, an effectivetool for assessing the operational health of a TNS includes severalfeatures. In one embodiment, the inventive tool selectively choosesvalues that are most indicative of the equipment's performance. Thistool may also appropriately weight each value according to each value'srelative contribution in assessing the operation health of the TNS. Inorder to get a comprehensive view of the operational health of the TNS,the inventive tool may consist not only of internal equipmentdiagnostics, but also may include external measurements of theequipment's operational health. This inventive tool may also determinean acceptable operational health window for the TNS based on actualhistorical data of the TNS being monitored. Using this historical dataprovides a more accurate prediction of operational health than usingdata determined from another TNS since it takes into account the uniquecharacteristics and operational environment of a given TNS.Additionally, the inventive tool not only has the capacity to determinethe current operational health, but also to make a projection of theoperational health and forecast the future TNS performance.

In one aspect of the current invention, a limited number of theavailable feedback values associated with a TNS are chosen for analysis.For convenience, in one embodiment, these values are classified underdifferent “parameters”, the parameters categorizing different aspects ofthe operational status, or health, of a TNS. These parameters include,in one embodiment, both equipment diagnostics as well as externalmeasurements of the operational health of the TNS. In this embodiment,all of the feedback-values used are related to the TNS under evaluation,as opposed to being generated from historical or real-time data fromanother TNS. The monitored parameters include: (a) internalswitch-performance-diagnostics and dial-delay, (b) capacity-utilizationof the TNS, (c) preventative-maintenance-routine performance, and (d)demand-maintenance required based on user complaints as a result of TNShardware or software defects.

Each parameter contains one or more selected values that in some waycharacterize an aspect of the equipment's operational health. Each ofthe specific values chosen to be included in the prediction is given arelative “weighting”, depending on its overall influence on theoperational health of the equipment. Using each of the chosenmonitored-values and multiplying these values by their relativeweighting, an overall prediction of the current operational health ofthe TNS is determined. In this embodiment, the predicted value for theoverall operation health, based on the chosen feedback from the TNS,accounts for at least 80% of the true operational health of the TNS.

In a further embodiment, an even more limited amount of parameters arechosen to predict the operational health of the TNS. The parameters inthis more limited list are called “preferred-parameters”. This listincludes: (a) internal-switch-performance-diagnostics and dial-delay,(b) capacity-utilization of the TNS, (c)preventative-maintenance-routine performance, and (d) demand-maintenancerequired based on user complaints as a result of TNS hardware orsoftware defects. In this embodiment, the predicted value for theoverall operational health, based on the chosen feedback from the TNS,accounts for at least 60% of the true operational health of the TNS.

In further embodiments of the invention, the actual weightings of theparameters or preferred-parameters are given, within a certain range.

In another embodiment, the values selected to be monitored for theparameter called “internal-switch-performance-diagnostics anddial-delay” are specified, depending on the actual manufacturer of theTNS used. A further embodiment includes listing the specific weightingsof each of these values.

A further aspect of the current invention involves not only determiningthe current operational health of the TNS, but also projecting theoperational health into the future. A further embodiment disclosesforecasting the future operational health for a specific amount of time,such as one month.

A first step in practicing an embodiment of the invention involvesdetermining for the TNS a range of acceptable values for the operationalhealth of the TNS. This range is determined by using at least twooperational-health-values from a set of operational-health-valuescontaining current and past operational-health-values. In oneembodiment, the range of acceptable values for the operational health isdetermined with the following procedure. A mean value is determined fora set of operation-health-values comprising current and pastoperational-health-values. Then, the value for three standard deviationsabove and below the mean is determined. The range for acceptableoperational-health-values is bracketed by the three standard deviationvalue above and below the mean. In another embodiment, the acceptablerange is determined based on experience instead of being based on threestandard deviations.

A statistical modeling technique called autoregressive modeling may beused to calculate a future operational-health-value. In one embodiment,multiple equations from the autoregressive models are chosen to beevaluated. Current and past values for the operational health can beused as test values to determine which model produces the lowestdeviation from the actual measured value for operational health. Themodel with the lowest deviation, also referred to as the lowest meansquare residual value, is chosen. The chosen autoregressive model isthen used to project a future value for the operational health. Thisforecasted value is then compared to the range of acceptable values foroperational health. In one embodiment, if the predicted value fallsoutside of the acceptable range, a user or other interested party isnotified. In another embodiment, the notification includes informationabout which element on the TNS is likely contributing to the predicteddeviation. Once a problematic element is determined, corrective actioncan be taken in order to prevent a catastrophic failure of the TNS fromoccurring.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing a Predictive Maintenance Tool (PMT) for aTelephone Network Switch (TNS).

FIG. 2 is a flowchart showing the conversion of multiple values, eachpartially indicative of an operational health of the TNS, into a singlevalue representing an overall operational health of the TNS.

DETAILED DESCRIPTION

In accordance with an aspect of this invention, the inventive tooldetermines a current operational health of a Telephone Network Switch(TNS) by selectively using only certain parameters. These selectedparameters, both internal and external to the TNS, are those that mostinfluence the operational health.

In a further aspect of this invention, the inventive tool projects theoperational health of the TNS into the future in order to forecast theTNS performance. In the two discussed aspects of the current invention,not only can a current or future deviation be determined, but also asource of the deviation is identified allowing for a focusedpreventative maintenance effort.

FIG. 1 is a flowchart, in 100, showing a Predictive Maintenance Tool fora Telephone Network Switch (TNS). At 101, the TNS is monitored byvarious systems with the resulting feedback-values for each window oftime of TNS operation sent to one or more databases. The feedback-valuescan be numerical or non-numerical, such as “yes”, “no”, or the soundingof an alarm. A feedback-value represents some aspect of the operationalhealth of the TNS. Upon receipt at the one or more databases, eachfeedback-value is designated, as shown at 102, as belonging to one offour “Preferred-Parameters” (PPs): (1) PP1: InternalSwitch-Performance-Diagnostics & Dial-delay, (2) PP2:Capacity-Utilization of the TNS, (3) PP3:Preventative-Maintenance-Routines Performance, and (4) PP4:demand-maintenance required based on user complaints as a result of TNShardware or software defects.

PP1 is a representation of machine generated diagnostics. For PP1, the“internal switch diagnostics” represent self-reporting diagnostics thata TNS vendor equips their switch with in order to indicate the presenceor absence of any fault activity in the TNS. Some diagnostics reflectalarms that exhibit a greater influence, i.e. bear more “weight”, on theoperation of the TNS. In determining the assignment of weights of thevarious feedback-values in PP1, factors that are considered aremanufacturer and industry standards and personal experience. In oneembodiment, PP1 for the Lucent line of switches is comprised of twelvesub-categories, all focused on internal switch diagnostics. The PP1 forthe Nortel line is composed of nine sub-categories. Seven are focused oninternal switch diagnostics, while two are focused on dial-delay.“Dial-delay” includes information concerning dial tone delay andincoming start dial-delay.

PP2 is a representation of capacity management of the TNS, and can alsobe called “final trunk group utilization”. It is the percentage of thenumber of trunk groups operating at greater than 90% capacity ascompared to the total number of in-service final trunk groups in theswitch. Customer-facing trunk groups are excluded.

PP3 reflects the influence technical personnel have on the operations ofthe switch. Various Preventative Maintenance routines are scheduled forcompletion on a weekly, monthly, quarterly, etc. basis. This PP is arepresentation of technical attention to preventative maintenanceroutines, i.e. the percentage of Preventative Maintenance routines that,over the last 30 calendar days, have been completed on time.

PP4 is a representation of a count of customer and/or monitoring centergenerated trouble tickets. The value describes percentage of troubletickets to in-service lines and trunks.

At 103, all of the non-numerical feedback-values are converted into anumerical value indicative of the feedback-value. For example, a “yes”or an activation of an alarm may be converted into a “one”, and a “no”or no activation of an alarm may be converted into a “zero”.

At 104, a process, as described further in this section under FIG. 2, isinitiated which converts all of the individual numerical representationsof each feedback-value into an operational-health-value. Theoperational-health-value represents the operational health of the TNSfor a window of time of TNS operation from which the TNS feed-backvalues were collected. This single value is used for severalcalculations and determinations. As previously noted, this calculationis based on selecting the feedback-values that most strongly indicatethe operational health of the TNS. This selection simplifies themonitoring of the TNS and the calculation of theoperational-health-value. However, since a good number offeedback-values are intentionally not included, i.e. those deemed to beless indicative of the operational health of the TNS, there is a certainamount of uncertainty associated with the operational-health-value. Indifferent embodiments of the invention, depending on how manyfeedback-values are included in the calculation of theoperational-health-value, a determination is made concerning how closelythe true operational health of the TNS is approximated by theoperational-health-value. In one embodiment, at least 80% of the trueoperational health is determined. In another embodiment, at least 60% ofthe true operational health is determined.

Some examples of uses for the operational-health-value are discussedlater in FIG. 1, and they include: (1) a determination of theoperational health of a TNS, (2) a component is determining a range ofacceptable operational-health-values for a TNS, and (3) as a componentin a data series of operational-health-values used to project a forecastof a future operational-health-value.

At 105, a determination of a range ofacceptable-operational-health-values is made for the TNS. First, a meanvalue is determined by averaging operational-health-values comprising ofcurrent and past operational-health-values. While a mean value can bedetermined with as little as two values, in one embodiment, more thantwo values are used to determine the mean value. In one embodiment,after a mean value is determined, three standard deviations from thismean value is determined. Acceptable-operational-health-values comprisevalues falling in the range within three standard deviations above andbelow the mean value. In another embodiment, the range is chosen basedon user experience.

At 106, the method continues, in one embodiment, by projecting theoperational-health of the TNS into the future. This forecast, in oneembodiment, is performed with an autoregressive (AR(n)) model. In oneembodiment, a group of (AR(n)) models are used and the one which bestfits a time series of operational-health-values is used to make theforecast. In one embodiment, the time series ofoperational-health-values is a series of operational-health-values thata spaced one day apart. Several statistical software programs can befound in the marketplace that use autoregressive modeling, also known asBox-Jenkins modeling, to analyze time series data. AR(n) models are aconvenient tool for better understanding a time series of data. In thecurrent embodiment, an AR(n) model is used to forecast future values ina time series of operational-health-values in order to forecast futureoperational-health-values.

However, before computing which AR(n) model best fits the time series,it needs to be determined if the time series ofoperational-health-values exhibits stationarity. Stationarity is astochastic process whose probability distribution is the same for alltimes. As a result, the mean value as well as standard deviations fromthe mean are constant over time. When a time series of data pointexhibits stationarity, a correlation between various values “k” daysapart depends only on “k”, and not any other trends in the data, such asseasonal or periodical trends. The calculation of stationarity isdescribed in the following steps.

First, a set of operational-health-values are determined over a givenperiod of time, and listed in a column. Second, a second column isformed, which is a replicated of the first column, where the first entryis blank, and all of the values in the second column are the same as thevalues in the first column, except that they are lagged one time periodbehind the first column. Third, calculate the mean of the observedoperational-health-values, called “Ī”.

$\overset{\_}{I} = \frac{\sum\limits_{t = 1}^{t = m}\; I_{t}}{m}$

“I,” is the operative-health-value for a given time. “m” is the numberof operational-health-values values in the time series, (the number maybe slightly less than the actual total number ofoperational-health-values in the time series due to removal ofoutliers).

Fourth, compute “r”, where:

$r = \frac{\sum\limits_{t = 1}^{t = {m - 1}}\;{\left( {I_{t} - \overset{\_}{I}} \right)\left( {I_{t + 1} - \overset{\_}{I}} \right)}}{\sum\limits_{t = 1}^{t = m}\left( {I_{t} - \overset{\_}{I}} \right)^{2}}$

“r” is the correlation coefficient between the observed series and theobserved series lagged once.

${''}{\sum\limits_{t = 1}^{t = {m - 1}}{''}}$is the sum of the terms from period “t=1” to period “t=m−1”.

Fifth, compute “z”

$z = {\left( \frac{\sqrt{m - 4}}{2} \right){\ln\left\lbrack \frac{\left( {1 + r} \right)}{\left( {1 - r} \right)} \right\rbrack}}$

“ln” is the natural log.

If the absolute value of “z” is found to be less than or equal to 2,i.e. |z|≦2, then the series would be declared stationary, and theoriginal set of operation-health-values would be used to decide withAR(n) model to use. However, if absolute value of “z” is found to begreater than 2, i.e. |z|>2, then the series is declared non-stationary.In this case, the “first-difference-values” is used in the determinationof which AR(n) model fits the data best. The first-difference-values arecomputed from the difference between each operational-health-value andthe previous operational-health-value. The same column setup establishedin the second step above can be used for this calculation.

Once a data series is identified, the last part of 106 is choosing theproper AR(n). The AR(n) used to forecast the future performance of a TNSis chosen from one of the five AR(n) models, which are: AR(1), AR(2),AR(3), AR(4), and AR(5). The AR(n) with the least Mean Square Residual(MSR) is the AR(n) used to forecast.

First, a matrix is established for each AR(n) to be modeled. The size ofthe matrix is a function of various factors: (1) the number ofoperational-health-values that are in the original data series, (2)whether actual operational-health-values or theirfirst-difference-values are used, and (3) which AR(n) is being tested.The number of columns created, in addition to the column containing theoriginal data series, is equal to “n” of AR(n). Each additional columnis a replicate of the previous column, except that all the values in theadded column are lagged one time period behind the first column. Thefirst row is therefore left blank, since there are no values to fill it.Therefore, if the original data series comprised of 20 values, the AR(1)model would be a 19×2 matrix, the AR(2) model would be an 18×3 matrix,and so forth.

Second, the X-matrix is formed. This is done by taking the above matrixfor the AR(n) model and replacing the values in the first column with“1”s and ignoring the information in the first “n” rows, “n” representsnumber of the AR(n) being tested.

Third, transpose the X-matrix, resulting in the X¹-matrix, where therows become the columns and the columns become the rows. This is knownas either the “X transpose” or the “X prime” matrix.

Fourth, multiply the two matrices, the X¹ matrix times the X matrix.This results in a “n+1” by “n+1” matrix, i.e. the number of rows of thefirst matrix and the number of columns of the second matrix. This iscalled the X¹X matrix, which is a square matrix, i.e. the number of rowsequals the number of columns.

Fifth, take the inverse of the X¹X matrix, which results in the (X¹X)⁻¹matrix, called the “X prime X inverse” matrix.

Sixth, make a vector by using the original data series, called the Yvector. It has all of the original data series, except for the first “n”rows. Multiply the X¹ matrix by the Y vector. This results in the X¹ Ymatrix, an “(n+1)” by “1” matrix.

Seventh, determine the b vector, also known as the regressioncoefficient vector, as follows:b=(X ¹ X)*(X ¹ Y )

The resultant b vector is an “(n+1)” by “1” array. The first value(found in row 1, column 1) is the “coefficient” or “coefficientintercept”. The second value (found in row 2, column 1) can be called “XVariable 1”. The number of “X Variable n” is dependent on which AR(n) isbeing modeled.

Eighth, Determine the predicted values, “Ŷ”, based on the “X” matrix,using the following formula:Ŷ=X*b

Ninth, determine the Vector of Residuals, “R”, as follows:R=Y−Ŷ

Tenth, take the transpose of “R”, giving “R¹”, called “R prime” or “Rtranspose”.

Eleventh, multiply “R¹*R” giving the Sum of Squares for Residuals (SS),which results in a scalar matrix, a “1×1” matrix.

Twelfth, determine the Mean Square Residual, as follows:MSR=SS/(Size of the Y vector−b vector)

The MSR for each of the five of the AR(n) models is compared. The modelwith the least MSR value is selected as the AR(n) model of choice.

Once an autoregressive model that minimizes the MSR has been determined,in one embodiment, at 107, the model is used to forecast afuture-operational-health-value of the TNS. First, the outliers thatwere ignored in determining the best fitting AR(n) model are nowun-ignored. Second, as mentioned above in the seventh step ofdetermining the AR(n) model of choice, the “b” vector, which is alsoknown as the regression coefficient vector, is determined. The firstvalue in the “b” vector, called here “A₀”, is the “coefficientintercept”. The next value, or values, in the “b” vector, called here“A₁, A₂, . . . , A_(n), is the “X Variable n” value. As example ofpredicting an operational-health-value for the TNS, if theautoregressive model selected is AR(3), then afuture-operational-health-value, OPHi_(P) at time “P”, would beforecasted as follows:

For predicted day 1 (Day 51)OPHi ₅₁ =A ₀ +A ₁ OPHi ₅₀ +A ₂ OPHi ₄₉ +A ₃ OPHi ₄₈

-   -   where OPHi₅₀ is the current-operational-health-value,    -   OPHi₄₈ and OPHi₄₉ are past-operational-health-values    -   A₀ is the “coefficient intercept”,    -   and A₁, A₂, A₃ are the “X Variable 1”, “X Variable 2”, and “X        Variable 3” values for AR(3)

For predicted day 2 (Day 52)OPHi ₅₂ =A ₀ +A ₁ OPHi ₅₁ +A ₂ OPHi ₅₀ +A ₃ OPHi ₄₉

-   -   where OPHi₅₁ is first future-operational-health-value

For predicted day 3 (Day 53)OPHi ₅₃ =A ₀ +A ₁ OPHi ₅₂ +A ₂ OPHi ₅₁ +A ₃ OPHi ₅₀

For predicted day 4 (Day 54)OPHi ₅₄ =A ₀ +A ₁ OPHi ₅₃ +A ₂ OPHi ₅₂ +A ₃ OPHi ₅₁

For predicted day 5 (Day 55)OPHi ₅₅ =A ₀ +A ₁ OPHi ₅₄ +A ₂ OPHi ₅₃ +A ₃ OPHi ₅₂

At 108, when a deviation is forecasted, the method calls for determiningwhich element is projected to contribute to the TNS failure. Thiselement can be pinpointed by analyzing the feedback-values that arebeing monitored.

When a future deviation is predicted and a contributing element isidentified, the method continues, at 109. A user is notified of anelement which is forecasted to contribute to thefuture-operational-health-value falling out of the range ofacceptable-operational-health-values. This enables corrective action tobe taken before TNS failure occurs.

FIG. 2, in 200, describes the generation of an operational-health-valuefor a TNS. This is done by putting the multiple feedback-values for theTNS through a series of calculations in order to convert thefeedback-values into a single operational-health-value. The methoddescribed in this figure is detailing step 104 on FIG. 1. Step 104 comesafter step 103, which converted all non-numerical feedback-values intonumerical values indicative of the feedback-values. Therefore, allreferences to “feedback-values” when describing FIG. 2 assumenon-numerical feedback-values have already been converted into numericalvalues representing the feedback-value.

At 201, the various preferred-parameters used in this embodiment arelisted. They are as follows: (a) PP1: internalswitch-performance-diagnostics & dial-delay, (b) PP2:capacity-utilization of the TNS, (c) PP3:preventative-maintenance-routine performance, and (d) PP4:demand-maintenance required based on user complaints relating to TNShardware or software defects.

At 202, the calculation of the operational-health-value begins with“step 1”. At step 1, those feedback-values that are affected by switchcall volume are “normalized”. This is done by dividing the feedbackvalue by daily call volume. Therefore, after normalization, the valuesare more independent of fluctuations in daily call volume than beforenormalization. This step only applies to the feedback-values categorizedin PP1 as “internal switch-performance-diagnostics” and all values inPP4. However, this does not apply to the feedback-values in PP1 for“dial-delay” and all values in PP2 and PP3, since they do not vary withswitch call volume.

Step 2, at 203, describes applying a weight to each of thefeedback-values classified under PP1 and PP4. These weightings representthe relative influence that each feedback-value has on the PP, andtherefore on the overall operational health of the TNS. The weightingsare assigned based on experience and industry specifications. Theapplication of weightings only applies to PP1 and PP4 since PP2 and PP3each only have one numerical-feedback-value. The application of a weightto the general PP categories relative to each other is done later instep 5, at 206.

PP1: Lucent 5ESS diagnostics:

-   -   a. AMA IRR:        -   Sub-category weight=9%    -   b. ONTC FAULTS:        -   Sub-category weight=15%    -   c. PDTO:        -   Sub-category weight=5%    -   d. AMA LOST:        -   Sub-category weight=9%    -   e. INITIALIZATIONS:        -   Sub-category weight=14%    -   f. MDII MESSAGES:        -   Sub-category weight=9%    -   g. DFC (DEFENSIVE CHECK FAILURES) and ASSERTS:        -   Sub-category weight=9%    -   h. SPP MESSAGES (Single Process Purge):        -   Sub-category weight=4%    -   i. AUDITS:        -   Sub-category weight=4%    -   j. RTA DCF ERROR:        -   Sub-category weight=4%    -   k. MANUAL ACTIONS:        -   Sub-category weight=9%    -   l. INTERUPTS:        -   Sub-category weight=9%

PP1: Nortel DMS-500:

-   -   a. SPMS (Switch Performance Monitoring System):        -   Sub-category weight=23%    -   b. AUDIT:        -   Sub-category weight=9%    -   c. ENET (Network Integrity Failure):        -   Sub-category weight=10%    -   d. LOST CALLS:        -   Sub-category weight=10%    -   e. SWACT:        -   Sub-category weight=10%    -   f. SWERR:        -   Sub-category weight=14%    -   g. TRAP:        -   Sub-category weight=14%    -   h. DTD:        -   Sub-category weight=6%    -   i. IDDD:        -   Sub-category weight=4%

PP2 & PP3: No sub categories

PP4:

-   -   a. Trouble ticket source number one:        -   Sub-category weight=50%    -   b. Trouble ticket source number two:        -   Sub-category weight=50%

In step 3, at 204 the numerical-feedback-values in each PP are summed toproduce a single value for each PP.

In step 4, at 205, a range adjustment is applied to the PP. Thisadjustment places an equivalent unit value across all thepreferred-parameters. Now, the individual values from each PP can becompared to each other.

Step 5, at 206, applies a weight to each PP, in a similar manner as wasdone at Step 2. This enables the relative contribution of each PP towarddetermining the overall operational health of the TNS to be properlytaken into consideration. In one embodiment, the weighting is asfollows:

PP1: 50% PP2: 15% PP3: 10% PP4: 25%

Only a select amount of the available parameters and feedback-valuesfrom the operation of the TNS have been chosen in these embodiments ofthe invention. By choosing to leave out parameters and feedback-values,the determination of the operational-health has been greatly simplified.Parameters left out are those that are deemed to have less than a 10%overall influence on an operational-health-value. Feedback-values leftout are those that are deemed to have less than a 2% overall influenceon an operational-health-value.

In step 6, at 207, the sum of the preferred-parameters is subtractedfrom 100 to generate the operational-health-value.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method of quantifying a determination of an operational health of aTelephone Network Switch (TNS), the method being performed by aprocessor executing computer program instructions, the methodcomprising: 1) the processor receiving feedback-values, both numericaland non-numerical, relating to the operational health of the TNS from aspecific window of time of operation of the TNS, said feedback-valuesbeing associated with parameters selected from the group consisting of:(a) internal switch-performance-diagnostics and dial-delay, (b)capacity-utilization of the TNS, (c) preventative-maintenance-routineperformance, and (d) demand-maintenance required based on usercomplaints relating to TNS hardware or software defects; 2) theprocessor converting all non-numerical feedback-values into a numericalvalue indicative of the feedback-value; 3) the processor assigning aweight to the numerical value for each feedback-value to reflect arelative importance of the feedback-value in determining the operationalhealth of the TNS; and, 4) the processor calculating anoperational-health-value, based on the numerical value for eachfeedback-value and the assigned weight for each feedback-value, whereinthe operational-health-value accounts for at least 80% of an accurateprediction of the operational health of the TNS; 5) wherein the totalassigned weight to the feedback-values in each preferred-parameter arein the following ranges: (a) 40-60% for the internalswitch-performance-diagnostics and dial-delay, (b) 5-25% for thecapacity-utilization of the TNS, (c) up to 20% for thepreventative-maintenance-routine performance, and (d) 15-35% for thedemand-maintenance required based on user complaints relating to TNShardware or software defects 6) and wherein the sum of the totalassigned weights does not exceed 100%.
 2. A method of quantifying adetermination of an operational health of a Telephone Network Switch(TNS), the method being performed by a processor executing computerprogram instructions, the method comprising: 1) the processor receivingfeedback-values, both numerical and non-numerical, relating to theoperational health of the TNS from a specific window of time ofoperation of the TNS, said feedback-values being associated withparameters selected from the group consisting of: (a) internalswitch-performance-diagnostics and dial-delay, (b) capacity-utilizationof the TNS, (c) preventative-maintenance-routine performance, and (d)demand-maintenance required based on user complaints relating to TNShardware or software defects; 2) the processor converting allnon-numerical feedback-values into a numerical value indicative of thefeedback-value; 3) the processor assigning a weight to the numericalvalue for each feedback-value to reflect a relative importance of thefeedback-value in determining the operational health of the TNS; and, 4)the processor calculating an operational-health-value, based on thenumerical value for each feedback-value and the assigned weight for eachfeedback-value, wherein the operational-health-value accounts for atleast 80% of an accurate prediction of the operational health of theTNS; 5) wherein when said specific window of time of the operation ofthe TNS is a window of time in which the most current feedback-valuesfrom the operation of the TNS are used to determine theoperational-health-value, said operational-health-value is called acurrent-operational-health-value; 6) wherein when said specific windowof time of the operation of the TNS is a window of time in which valuesfrom before the most current feedback-values from the operation of theTNS are used to determine the operational-health-value, saidoperational-health-value is called a past-operational-health-value; 7)wherein when said specific window of time of the operation of the TNS isa window of time that has not yet occurred, a projectedoperational-health-value is called a future-operational-health-value, 8)and wherein a future operational health of a TNS is forecasted by, a)the processor determining, for the TNS, a range ofacceptable-operational-health-values, wherein said determining of therange of acceptable-operational-health-values is calculated based onusing at least two operational-health-values from a set ofoperational-health-values containing current and pastoperational-health-values; b) the processor forecasting, for the TNS,the future-operational-health-value, wherein said forecasting of thefuture-operational-health-value is calculated based on thecurrent-operational-health-value and at least onepast-operational-health-value; and, c) the processor comparing thefuture operational-health-value with the range ofacceptable-operational-health-values.
 3. The method of claim 2, furthercomprising the processor determining, in a situation where thefuture-operational-health-value falls outside the range ofacceptable-operational-health-values, an element of the TNS that iscontributing to the future-operational-health-value falling outside ofthe range of acceptable-operational-health-values.
 4. The method ofclaim 3, further comprising the processor informing a user as to theidentification of the element of the TNS that is contributing to thefuture-operational-health-value falling outside of the range ofacceptable-operational-health-values.
 5. The method of claim 2, wherein,when calculating of the range of acceptable-operation-health-values: amean value is determined, based on the current and pastoperational-health-values; and, the range ofacceptable-operational-health-values is three standard deviations higherand lower than the mean value.
 6. A non-transitory computer readablemedium storing computer program instructions which, when executed on aprocessor, perform the steps of: 1) receiving feedback-values, bothnumerical and non-numerical, relating to the operational health of theTNS from a specific window of time of operation of the TNS, saidfeedback-values being associated with parameters selected from the groupconsisting of (a) internal switch-performance-diagnostics anddial-delay, (b) capacity-utilization of the TNS, (c)preventative-maintenance-routine performance, and (d) demand-maintenancerequired based on user complaints relating to TNS hardware or softwaredefects; 2) converting all non-numerical feedback-values into anumerical value indicative of the feedback-value; 3) assigning a weightto the numerical value for each feedback-value to reflect a relativeimportance of the feedback-value in determining the operational healthof the TNS; and, 4) calculating an operational-health-value, based onthe numerical value for each feedback-value and the assigned weight foreach feedback-value, wherein the operational-health-value accounts forat least 60% of an accurate prediction of the operational health of theTNS, 5) wherein when said specific window of time of the operation ofthe TNS is a window of time in which the most current feedback-valuesfrom the operation of the TNS are used to determine theoperational-health-value, said operational-health-value is called acurrent-operational-health-value; 6) wherein when said specific windowof time of the operation of the TNS is a window of time in which valuesfrom before the most current feedback-values from the operation of theTNS are used to determine the operational-health-value, saidoperational-health-value is called a past-operational-health-value; 7)wherein when said specific window of time of the operation of the TNS isa window of time that has not yet occurred, a projectedoperational-health-value is called a future-operational-health-value, 8)and wherein a future operational health of a TNS is forecasted by a)determining, for the TNS, a range ofacceptable-operational-health-values, wherein said determining of therange of acceptable-operational-health-values is calculated based onusing at least two operational-health-values from a set ofoperational-health-values containing current and pastoperational-health-values; b) forecasting, for the TNS, thefuture-operational-health-value, wherein said forecasting of thefuture-operational-health-value is calculated based on thecurrent-operational-health-value and at least onepast-operational-health-value; and c) comparing the futureoperational-health-value with the range ofacceptable-operational-health-values.
 7. The non-transitory computerreadable medium of claim 6 wherein the program instructions, whenexecuted on the processor, perform the further step of determining, in asituation where the future-operational-health-value falls outside therange of acceptable-operational-health-values, an element of the TNSthat is contributing to the future-operational-health-value fallingoutside of the range of acceptable-operational-health-values.
 8. Thenon-transitory computer readable medium of claim 7 wherein the programinstructions, when executed on the processor, perform the further stepof informing a user as to the identification of the element of the TNSthat is contributing to the future-operational-health-value fallingoutside of the range of acceptable-operational-health-values.